Chinese-as-a-First-Language (CL1) and English-as-a-First-Language (EL1) Undergraduate Students' Business Writing in Canadian Universities: A Corpus-Based Contrastive Study of Linguistic Features
Notice bibliographique
Résumé
The importance of formulaic language, such as Lexical bundles (LBs) (e.g., as a result of, the value of the), the introductory it patterns (e.g., it is important to), and self-mention markers (e.g., I, me, you) in academic writing have been well recognized (Guan, 2022; Hyland, 2002b, 2005; Larsson, 2017). Those linguistic patterns are essential for organizing texts, constructing writers’ arguments, and projecting their voices in academic prose (Güngör, 2019; Hyland, 2002; Zhang, 2015). Nevertheless, the investigation of LBs, the introductory it patterns, and self-mention markers used by English-as-an-additional-language (EAL) undergraduates is limited. \nIn Canada, the number of Chinese-as-a-first-language (CL1) undergraduate students in the business major has increased significantly (CBIE, 2022). Given that LBs, introductory it patterns, and self-mention markers are challenging for CL1 students (Leedham, 2011), it is crucial for researchers and practitioners to understand those linguistics features used by CL1 students in the business discipline compared to English-as-a-first-language (EL1) students. Through comparative analysis, this study aims to provide greater insights into the structural and functional uses of LBs, the introductory it patterns, and self-mention markers used by CL1 and EL1 business students. \nSpecifically, the current study aims to fill the gap by analyzing the most frequent 4-word LBs, the introductory it patterns, and self-mention markers in CL1 and EL1 undergraduate students’ business writing concerning the frequency, structures, and functions of those linguistic features. The two self-compiled corpora, EL1 corpus, and CL1 corpus, including 42 articles in each corpus, were collected from second-year university-level business writing courses. Those linguistic patterns were analyzed quantitatively and qualitatively using the corpus analysis software AntConc (Anthony, 2023). \nThe results suggest that CL1 students showed significantly higher use and more variation of LBs and self-mention markers than EL1 students, while EL1 students employed significantly more instances with introductory it patterns. Regarding LBs, the use of LBs in EL1 and CL1 writing was similar, with a heavy reliance on verb-based phrases, indicating undergraduate students’ writing style as immature learner writing (Chen & Baker, 2010, 2016). With respect to the introductory it patterns, the introductory it has two prominent interpersonal roles in stance marking and interpreting observations. The main differences between the two corpora are in using the introductory it to hedge a claim and emphasize the writer’s attitude, with CL1 students making fewer hedges and overt persuasive statements. Concerning self-mention markers, the first-person pronoun I was the most frequent self-mention marker, followed by we in both corpora. The functions of self-mention markers used by both groups are primarily associated with low-risk functions, including expressing self-benefits and explaining procedures. Since limited uses of those linguistic patterns were identified in both corpora, the findings suggest pedagogical implications for teaching LBs, introductory it patterns, and self-mention markers in the business writing curriculum for CL1 and EL1 undergraduates.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».